The University of California San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences Drug Discovery course brings you lectures from both faculty and industry experts. With this course, recorded on campus at UCSD, we seek to share our access to top people in the field who bring an unprecedented range of expertise on drug discovery. In this course you will learn the drug discovery process up to the filing of an Initial New Drug Application or IND. Each week you will learn the steps that a pharmaceutical or biotech company goes through to discover a new therapeutic drug. In this course you will be able to: * Understand the pharmaceutical and biotechnology market a changing landscape * Learn the major aspects of the drug discovery process, starting with target selection, to compound screening to designing lead candidates. * Recognize current modern drug discovery based on the lock-and-key theory, which attempts to use one single compound to hit one target to combat the related disease. * Increase understanding of the various drug discovery tools and methods that are used for finding, identifying and designing a new drug. * Define and understand the regulatory responsibilities for drug discovery to file an Investigational New Drug Application (IND). This course is intended as part 1 of a series: Drug Discovery, Drug Development (https://www.coursera.org/learn/drug-development) and Drug Commercialization (https://www.coursera.org/learn/drug-commercialization). We would highly recommend that you take the courses in order since it will give you a better understanding on how a drug is discovered in the lab before being tested in clinical trials and then launched in the market place.
Drug Discovery: Proteomics, Genomics I
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Drug Discovery
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Drug Discovery: Proteomics, Genomics
This week we will be hearing from Phillip Bourne, PhD. Dr. Bourne was, at the time of this recording, the Associate Vice Chancellor for Innovation and Industrial Alliances in the UCSD Skaggs School of Pharmacy and Pharmaceutical Sciences. Since that time, he has been named the first Associate Director for Data Science at the United States National Institutes of Health.
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Williams S. Ettouati, Pharm.D.Director, Industrial Relations & Development; Health Sciences Associate Clinical Professor, N.S.
Skaggs School of Pharmacy and Pharmaceutical Sciences
Joseph D. MaAssociate Professor of Clinical Pharmacy
Skaggs School of Pharmacy and Pharmaceutical Sciences
[BLANK AUDIO]. Hello everyone.
my name is Philip Borne. I'm actually professor in the Skaggs
School of Pharmacy. I do early stage drug discovery.
I'm also the associate vice-chancellor for innovation and industrial alliances
here on campus. And that's a new role for me.
Something since this class was given last year, and I have quite a different
perspective on things than, than I had before as a result of that.
And I'm not going to touch on too much of that today.
But it's clearly something that is nagging at me right now in terms of how
we get things out of the university. into clinical trials and of course
beyond. I think there's some huge challenges
there. And I've, I've heard a bit about, you've
touched on that already. And perhaps we can talk about that at the
end. Okay, so I'm going to focus on the idea
of Proteomics and Genomics and what that brings.
and how that's perturbing the drug discovery process.
And I would say that you've kind of heard a bit of this, let me just cast it in my
own way. By saying it's the best of times and in
someways it's the worse of times. And I love this, this is the opening
paragraph from Charles Dickens book. And it's so at [UNKNOWN] for drug
discovery, it's unbelievable. It was the best of times, it was the
worst of times, it was the age of wisdom. And I think Genomics and Prodiomics is
kind of giving us. The age of wisdom, but it is also the age
of foolishness in how we've so far been out, to take a lot of that forward.
Because we haven't changed the way we do things in large part in the drug
discovery process, even in light of this new data and these new capabilities, in
my opinion. And I'll give you some illustrations of
why I think that's breaking down. and I could go onto this with the winter
of despair, and so on. And, but here's the sort of best of
times, right? We are in this Omics Revolution.
It started in about 1990, with the, with the human genome project, which took
quite a long time to actually bear fruit after it was finished in 2000.
But there's no question, and you asked, there's no doubt.
And you asked the question about personalized medicine, that as we move
down we're at, whatever it is now, three to $5,000 for a genome.
A human genome, we will be down at the $50 level in the next several years.
That is going to have, undoubtedly, a profound impact, on, on healthcare, in
times to come. So, from that point of view, it's, it's
the best of times. And we'll see what that means in terms of
data and integration. And the kinds of other questions you were
asking. In some ways, it's sort of the best of
times, because you know we have scenes through NIH and other places.
And, and the private sector large increases in research budgets.
In principle spurn by the human gene among other things.
To actually do better at discovering drugs.
But when as you already heard, I'm sure that you'll continue to hear throughout
this, the success rate is abysmal. And you know, so this is the, another
sort of illustration of the best of times and the worst of times.
So, putting that into words you know we have this explosion of data which in
principle allows us to do a lot we, we are starting to get.
And I'm going to emphasize this, we're starting to get to an understanding of
complex systems in ways that we never have before.
That should be impacting the drug discovery process.
At this point in time, I don't think it is, but I think we're on the cusp of a
turnover, and I'll say more about that. And of course the information technology
that's needed to do all this, which is an area where I spend quite a lot of time
myself, is obviously improving. so, let optimisim rule.
So, what this all means is you know, this whole traditional model that's been based
on on the computational side at least, computational chemistry and
cheminformatics. We're now bringing the idea of
bioinformatics, and systems biology studying the whole system particularly in
silico, into play into this whole process.
And the end result of that is something I would call Systems Pharmacology.
And it's actually doing pharmacology in a different way than we've done before
which I'll illustrate. And I'll show you a couple of examples of
what I mean by that, and why I think it's so important.
All right, so what's my agenda? I'll give you a little sense of you know,
I think what you're going to get out of this course is because this is the nature
of the game right now. You're going to get from different
people, you're going to get some mixed messages.
Their opinions are going to be somewhat different about where drug discovery and
the whole industry's going. So, I'm just giving you my perspective.
I'm going to tell you where it comes from and you can, you can take it or leave it.
That's, you know, that's the business. I'll say a little bit about the Omics
revolution in a bit more detail. Something about, something that I'm very
passionate about, and something that I think it's also driving some change.
And anything that drives change I love. So, the notion of open science, and I
mentioned the IT revolution, but open science is also something that's
changing. Drug companies are becoming, at some
levels, much more open than they ever were before.
And that's basically out of necessity, because of failure, in my opinion.
Again, this is biased coming in, right. and then what impact does all that have
on drug discovery? We'll look to that and I'll give you a
couple of examples. Because how could I possibly stand here
and not talk about my own work. Oh, people were, of course I don't
actually do anything, but the work of people in the lab.
and then there's, you know, a few words of caution about everything I've said,
which probably means it's not true at all.
All right, so this is we, what we do is, I maintain a resource, an open resource
called the Protein Data Bank. Which is the repository for all meca
molecular structure information, proteins DNA, RNA.
and that there hasn't been a drug discovered in the last 30 years, that
hasn't used that quite extensively. And that's given me a certain view point
in all of this. we distribute a ton of data there are
seven structures looked at every second essentially around the world.
and then we've been using that ourselves in a research perspective to to do what
I, already introduced to you the notion of systems pharmacology.
So, you know personally I mention the open science.
Also I've started some companies while I've been here, which is one of the
reasons why I'm doing this associate vice chancellor job right now.
because you'll learn as you go on the more you complain about something, if you
complain long enough. Before you know it, you're part of the
solution. So, you know trying to form companies
here is not totally straightforward. having you know, finally the chance that
says we don't like it, fix it. So, that's kind of what I'm trying to do
right now. so that sort of some background.
So, let's look at the Omics Driver. So, right now we're in a very interesting
situation associated with data particular genomic data but it's true of all data in
biology. and beyond for that matter.
we're completely out of whack, with the ability to handle that data.
So, the data there's a you know what Moore's law is?
Most of you, so Moore's law is just you know a reflection of the dropping cost
per some unit of computing over time. And essentially what you could do with a,
a unit of computing whether it be storage or CPU was you know essentially that
doubles every 18 months. And that it's gone between eight 12 and
24 months, but 18 is sort of average. And biological data of all elks, and the
whole notion of translation, and having data, not just at the Omics level but at
higher levels. you know, all of that data, for a long
time that was essentially keeping track with paralleling, Moore's law.
So, the cost of maintaining that data was essentially on par, hadn't changed over
time. That is no longer true.
In fact with forth generation sequencing, the new types of sequencing technologies.
The amount of data far, far outweighs, you know, these are, this is an
exponential scale. And you can see, you know, sequencing
sort of was paralleling this for quite a while.
And then, you know, first of all, were second and third and fourth generation,
that's really gone completely wacky. Right now, the amount of sequence data
that we're generating is doubling every five months.
It's absolutely huge, and that, that doesn't speak to any of the data's of
greater complexity, which I'll get to in a minute.
No one is really addressing the issues of what we keep, what we throw away.
We're not addressing issues associated with the complexity of that data, the
errors in that data. How we represent metadata, how we
represent prominence which is, how we describe the value of that data and who
owns it. All of these things, are completely out
of whack right now.
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